IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved matched filter for blood vessel detection of digital retinal images
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation
IEEE Transactions on Image Processing
Retinal vessel extraction with the image ray transform
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Retinal vessel extraction using first-order derivative of Gaussian and morphological processing
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Retinal vessel segmentation using a multi-scale medialness function
Computers in Biology and Medicine
MFCA: matched filters with cellular automata for retinal vessel detection
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Blood vessel segmentation methodologies in retinal images - A survey
Computer Methods and Programs in Biomedicine
An approach to localize the retinal blood vessels using bit planes and centerline detection
Computer Methods and Programs in Biomedicine
A Study of Cloud Computing for Retinal Image Processing Through MATLAB
International Journal of Cloud Applications and Computing
Automatic vessel network features quantification using local vessel pattern operator
Computers in Biology and Medicine
Computer-aided diagnosis of diabetic retinopathy: A review
Computers in Biology and Medicine
Power line detection from optical images
Neurocomputing
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Accurate extraction of retinal blood vessels is an important task in computer aided diagnosis of retinopathy. The matched filter (MF) is a simple yet effective method for vessel extraction. However, a MF will respond not only to vessels but also to non-vessel edges. This will lead to frequent false vessel detection. In this paper we propose a novel extension of the MF approach, namely the MF-FDOG, to detect retinal blood vessels. The proposed MF-FDOG is composed of the original MF, which is a zero-mean Gaussian function, and the first-order derivative of Gaussian (FDOG). The vessels are detected by thresholding the retinal image's response to the MF, while the threshold is adjusted by the image's response to the FDOG. The proposed MF-FDOG method is very simple; however, it reduces significantly the false detections produced by the original MF and detects many fine vessels that are missed by the MF. It achieves competitive vessel detection results as compared with those state-of-the-art schemes but with much lower complexity. In addition, it performs well at extracting vessels from pathological retinal images.